Construction of Knowledge Base for Emergency Power Supply Guarantee Mechanism and Intelligent Decision Support System Based on Knowledge Graph
Main Article Content
Abstract
This paper combines knowledge graph and intelligent decision-making technologies to conduct research on the construction of an emergency power supply guarantee knowledge base and an intelligent decision support system. Firstly, the paper sorts out the relevant theories and technologies of emergency power supply guarantee, knowledge graph and intelligent decision-making, completes domain ontology modeling, carries out collection, cleaning, knowledge extraction and fusion of multi-source heterogeneous data, and builds a standardized emergency power supply guarantee knowledge base. Secondly, adopting hybrid knowledge reasoning and multi-objective optimization algorithms, the overall design, function development and deployment of the intelligent decision support system are completed based on the front-end and back-end separation architecture. Finally, functional, performance and security tests are conducted, and a comparative analysis is carried out combined with a real-world case of power outage caused by a typhoon disaster. The results show that the system can realize the intelligent processing of the whole process including fault diagnosis, resource scheduling and emergency plan generation. Compared with the traditional manual mode, the emergency response speed, resource utilization rate and power supply reliability are significantly improved. This study can provide technical reference and practical experience for the intelligent construction of power emergency power supply guarantee.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
B. Wu, X. Wang, S. Qi, et al., “Emergency backup power robust planning for urban agglomeration power grids with a high proportion of new energy sources in extreme disaster scenarios,” Electr. Power Syst. Res., vol. 247, p. 111715, 2025, doi: 10.1016/J.EPSR.2025.11 1715.
C. Z. Chen and M. C. Li, “Energy emergency scheduling under extreme weather events: A novel emergency scheduling method based on the improved supernetwork,” Energy, vol. 322, p. 135491, 2025, doi: 10.1016/J.ENERGY.2025.135491.
R. Fan and P. Zhang, “A GA-SLP-Based Dynamic Allocation Method for Electric Power Emergency Materials Considering Disaster Impact Differences,” Int. J. Comput. Intell. Syst., vol. 18, no. 1, p. 281, 2025, doi: 10.1007/S44196-025-01018-9.
S. Li, C. Zhang, G. Yang, et al., “A knowledge modeling method for high-speed railway emergency faults based on structured logic diagrams and knowledge graphs,” High-speed Railw., vol. 4, no. 1, pp. 59–67, 2026, doi: 10.1016/J.HSPR.2025.10.004.
M. Zheng, Z. Chen, J. Li, et al., “Knowledge Graph and LSTM-Based Risk Identification Framework for Relay Protection in Power Systems: Fault Detection, Fault Location Estimation and Predictive Analytics,” J. Circuits Syst. Comput., vol. 35, no. 16, 2026, doi: 10.1142/S0218126626500908.
D. Jesse, M. Salman, and K. Benjamin, “Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review,” Energies, vol. 14, no. 20, p. 6476, 2021, doi: 10.3390/EN14206476.
A. Dubey, “Preparing the Power Grid for Extreme Weather Events: Resilience Modeling and Optimization,” in Women in Engineering and Science, pp. 209–243, 2023, doi: 10.1007/978-3-031-29724-3_8.
J. Zuo, C. Xu, W. Wang, et al., “Emergency Power Supply Restoration Strategy of Distribution Network Considering Operational Risk of Islanded Microgrid,” Processes, vol. 14, no. 3, p. 480, 2026, doi: 10.3390/PR14030480.
X. Zhao, “Research on multi-objective scheduling optimization of power system based on hybrid model of genetic algorithm and particle swarm optimization,” Discov. Comput., vol. 29, no. 1, p. 209, 2026, doi: 10.1007/S10791-026-09929-7.
D. Wu and Q. Liu, “Temporal knowledge graph-based spatiotemporal reasoning for fault diagnosis in smart grids,” Electr. Power Syst. Res., vol. 260, p. 113299, 2026, doi: 10.1016/J.EPSR.2026.113299.
T. Jiapeng, S. Hui, S. Gehao, et al., “An event knowledge graph system for the operation and maintenance of power equipment,” IET Gener. Transm. Distrib., vol. 16, no. 21, pp. 4291–4303, 2022, doi: 10.1049/GTD2.12598.
A. N. Qirim, M. Majdalawieh, B. A. Hani, et al., “Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks,” Int. J. Eng. Bus. Manag., vol. 17, 2025, doi: 10.1177/18479790251328183.
N. Yu, X. Hui, H. Yihua, et al., “Energy Router for Emergency Energy Supply in Urban Cities: A Review,” Power Electron. Drives, vol. 7, no. 1, pp. 246–266, 2022, doi: 10.2478/PEAD-2022-0019.
Z. Liu, W. Chen, S. Liu, et al., “Design of Hydrogen-Powered Mobile Emergency Power Vehicle with Soft Open Point and Appropriate Energy Management Strategy,” Appl. Syst. Innov., vol. 8, no. 6, p. 167, 2025, doi: 10.3390/ASI8060167.
J. Shaoxiong, P. Shirui, C. Erik, et al., “A Survey on Knowledge Graphs: Representation, Acquisition, and Applications,” IEEE Trans. Neural Netw. Learn. Syst., 2021, doi: 10.1109/TNNLS.2021.30708 43.
Z. Li, “Integrating BERT-XL with multi-dimensional knowledge graphs for knowledge completion and relation reasoning in archival fragmented texts,” Sci. Rep., 2026, doi: 10.1038/S41598-026-52038-0.
J. Monteiro, F. Sá, and J. Bernardino, “Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and Tiger-Graph,” Appl. Sci., vol. 13, no. 9, 2023, doi: 10.3390/APP13095770.